Last updated: 2022-02-21

Checks: 7 0

Knit directory: CePTER_RNASeq/

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Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.Rhistory
    Ignored:    analysis/MDSplots/D62_mdsplots.RData
    Ignored:    data/.Rhistory
    Ignored:    data/Countmatrix.RData
    Ignored:    oldcode/
    Ignored:    output/D62_GOresWGCNA.xlsx
    Ignored:    output/D62_ResTabs_KO.RData
    Ignored:    output/D62_ResTabs_KO_WGCNA.RData
    Ignored:    output/D62_WGCNA_adj_TOM.RData
    Ignored:    output/D62_dds_matrix.RData
    Ignored:    output/GOres_Comparisons.xlsx
    Ignored:    output/GOres_D244DIFFRAPA.xlsx
    Ignored:    output/GOres_D244DIFFnoRAPA.xlsx
    Ignored:    output/GOres_D244noDIFFRAPA.xlsx
    Ignored:    output/GOres_D244noDIFFnoRAPA.xlsx
    Ignored:    output/GOres_D62DIFFRAPA.xlsx
    Ignored:    output/GOres_D62DIFFnoRAPA.xlsx
    Ignored:    output/GOres_D62noDIFFRAPA.xlsx
    Ignored:    output/GOres_D62noDIFFnoRAPA.xlsx
    Ignored:    output/GOres_DIFFRAPA.xlsx
    Ignored:    output/GOres_DIFFnoRAPA.xlsx
    Ignored:    output/GOres_ReNDIFFRAPA.xlsx
    Ignored:    output/GOres_ReNDIFFnoRAPA.xlsx
    Ignored:    output/GOres_ReNnoDIFFRAPA.xlsx
    Ignored:    output/GOres_ReNnoDIFFnoRAPA.xlsx
    Ignored:    output/GOres_noDIFFRAPA.xlsx
    Ignored:    output/GOres_noDIFFnoRAPA.xlsx
    Ignored:    output/ResTabs_KO.RData
    Ignored:    output/ResTabs_KO_WGCNA.RData
    Ignored:    output/Restab_D244_DIFF_RAPA.xlsx
    Ignored:    output/Restab_D244_DIFF_noRAPA.xlsx
    Ignored:    output/Restab_D244_noDIFF_RAPA.xlsx
    Ignored:    output/Restab_D244_noDIFF_noRAPA.xlsx
    Ignored:    output/Restab_D62_DIFF_RAPA.xlsx
    Ignored:    output/Restab_D62_DIFF_noRAPA.xlsx
    Ignored:    output/Restab_D62_noDIFF_RAPA.xlsx
    Ignored:    output/Restab_D62_noDIFF_noRAPA.xlsx
    Ignored:    output/Restab_ReN_DIFF_RAPA.xlsx
    Ignored:    output/Restab_ReN_DIFF_noRAPA.xlsx
    Ignored:    output/Restab_ReN_noDIFF_RAPA.xlsx
    Ignored:    output/Restab_ReN_noDIFF_noRAPA.xlsx
    Ignored:    output/Restab_Repl_All_DIFF_RAPA.xlsx
    Ignored:    output/Restab_Repl_All_noDIFF_noRAPA.xlsx
    Ignored:    output/Restab_Repl_D62D244_DIFF_RAPA.xlsx
    Ignored:    output/Restab_Repl_D62D244_DIFF_noRAPA.xlsx
    Ignored:    output/Restab_Repl_D62D244_noDIFF_RAPA.xlsx
    Ignored:    output/Restab_Repl_D62D244_noDIFF_noRAPA.xlsx
    Ignored:    output/WGCNA_adj_TOM.RData

Untracked files:
    Untracked:  Wrapper.sh
    Untracked:  data/HumanM1 singlecellseq/
    Untracked:  data/genelists_to_test/
    Untracked:  mattson/
    Untracked:  wflow_helper.R

Unstaged changes:
    Modified:   output/dds_matrix.RData

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Preprocessing

#Load Count Matrix
if (file.exists(secret)){
    source(secret)
  } else {
    UID    = rstudioapi::askForPassword("fuchs user")
    PWD    = rstudioapi::askForPassword("fuchs password")
  }

filetarget= paste0(home,"/data/Countmatrix.RData")
if(!file.exists(filetarget)){
  url="sftp://fuchs.hhlr-gu.de/scratch/fuchs/agmisc/chiocchetti/RNASeq_Data/Cepter/Output/Countmatrix.RData"
  bin = getBinaryURL(url, userpw=paste0(UID,":",PWD))
  writeBin(bin, filetarget)
  load(filetarget)
} else {
  load(filetarget)
}

Ntot= nrow(Countdata)


#merge non unique annotations
if(length(unique(rownames(Countdata))) != Ntot){
  Countdata = Countdata %>% dplyr::group_by(row.names(Countdata)) %>% summarise_each(sum)
  Ntot= nrow(Countdata)
}


hgnc=gconvert(query=as.numeric(rownames(Countdata)), 
              organism = "hsapiens", 
              numeric_ns = "ENTREZGENE_ACC",
              target = "HGNC")


Ids = hgnc %>%  dplyr::select(name, input, description) %>% group_by(input) %>% 
  dplyr::summarise(name=paste(name, sep="; ", collapse = ";"), description = dplyr::first(description))

rowdescription = data.frame(entrez_gene = Ids$input, 
                            hgnc=Ids$name, 
                            description=Ids$description)


if(! all(row.names(Countdata) %in% rowdescription$entrez_gene)){
  
  rowdescription = rowdescription[match(row.names(Countdata), rowdescription$entrez_gene),]
  rownames(rowdescription)=row.names(Countdata)
}


# load and parse sample information 
SampleInfo=read.csv2(paste0(home,"/data/Sample_info_CePTER_RNASeq.csv"), 
                     row.names = 1)

SampleInfo$Row=gsub("[0-9]*","",SampleInfo$Position)
SampleInfo$Col=as.numeric(gsub("[A-Z]*","",SampleInfo$Position))

# set factors and relevel
SampleInfo$CellLine = as.factor(SampleInfo$CellLine)

SampleInfo$gRNA = paste0("sg",SampleInfo$gRNA)
SampleInfo$gRNA = factor(SampleInfo$gRNA, levels=c("sgNTC", "sg2.1", "sg2.2"), 
                         labels=c("sgNTC", "sg2.1", "sg2.2"))
SampleInfo$gRNA = relevel(SampleInfo$gRNA,ref="sgNTC" )

SampleInfo$KO = factor(SampleInfo$KO, levels=c(T,F), labels=c("KO", "WT"))
SampleInfo$KO = relevel(SampleInfo$KO,ref="WT" )

SampleInfo$DIFF = factor(SampleInfo$DIFF, levels=c(TRUE,FALSE), 
                         labels=c("DIFF", "noDIFF"))
SampleInfo$DIFF = relevel(SampleInfo$DIFF,ref="noDIFF")

SampleInfo$RAPA = factor(SampleInfo$RAPA, levels=c(T,F), 
                         labels=c("RAPA", "noRAPA"))

SampleInfo$RAPA = relevel(SampleInfo$RAPA,ref="noRAPA")

SampleInfo$label = with(SampleInfo, paste(CellLine,gRNA,DIFF,RAPA, sep="_"))
SampleInfo$fastQID = rownames(SampleInfo)
SampleInfo = SampleInfo %>% dplyr::group_by(label) %>% mutate(replicate=seq(n())) %>% as.data.frame()
SampleInfo$label_rep=with(SampleInfo, paste(label,replicate,sep="_"))
rownames(SampleInfo)=SampleInfo$fastQID

# align datasets
checkfiles = all(rownames(SampleInfo) %in% colnames(Countdata))
IDs=intersect(rownames(SampleInfo), colnames(Countdata))
Countdata = Countdata[,IDs]
SampleInfo = SampleInfo[IDs, ]

SampleInfo$reads_per_sample = colSums(Countdata)

display_tab(head(Countdata))
DE10NGSUKBR112901 DE80NGSUKBR112902 DE53NGSUKBR112903 DE26NGSUKBR112904 DE96NGSUKBR112905 DE69NGSUKBR112906 DE42NGSUKBR112907 DE15NGSUKBR112908 DE85NGSUKBR112909 DE58NGSUKBR112910 DE31NGSUKBR112911 DE04NGSUKBR112912 DE74NGSUKBR112913 DE47NGSUKBR112914 DE20NGSUKBR112915 DE90NGSUKBR112916 DE63NGSUKBR112917 DE36NGSUKBR112918 DE09NGSUKBR112919 DE79NGSUKBR112920 DE52NGSUKBR112921 DE25NGSUKBR112922 DE95NGSUKBR112923 DE68NGSUKBR112924 DE41NGSUKBR112925 DE14NGSUKBR112926 DE84NGSUKBR112927 DE57NGSUKBR112928 DE30NGSUKBR112929 DE03NGSUKBR112930 DE73NGSUKBR112931 DE46NGSUKBR112932 DE19NGSUKBR112933 DE89NGSUKBR112934 DE62NGSUKBR112935 DE35NGSUKBR112936 DE08NGSUKBR112937 DE78NGSUKBR112938 DE51NGSUKBR112939 DE24NGSUKBR112940 DE94NGSUKBR112941 DE67NGSUKBR112942 DE40NGSUKBR112943 DE13NGSUKBR112944 DE83NGSUKBR112945 DE56NGSUKBR112946 DE29NGSUKBR112947 DE02NGSUKBR112948 DE72NGSUKBR112949 DE45NGSUKBR112950 DE18NGSUKBR112951 DE88NGSUKBR112952 DE61NGSUKBR112953 DE34NGSUKBR112954 DE07NGSUKBR112955 DE77NGSUKBR112956 DE50NGSUKBR112957 DE23NGSUKBR112958 DE93NGSUKBR112959 DE66NGSUKBR112960 DE39NGSUKBR112961 DE12NGSUKBR112962 DE82NGSUKBR112963 DE55NGSUKBR112964 DE28NGSUKBR112965 DE98NGSUKBR112966 DE71NGSUKBR112967 DE44NGSUKBR112968 DE17NGSUKBR112969 DE87NGSUKBR112970 DE60NGSUKBR112971 DE33NGSUKBR112972 DE06NGSUKBR112973 DE76NGSUKBR112974 DE49NGSUKBR112975 DE22NGSUKBR112976 DE92NGSUKBR112977 DE65NGSUKBR112978 DE38NGSUKBR112979 DE11NGSUKBR112980 DE81NGSUKBR112981 DE54NGSUKBR112982 DE27NGSUKBR112983 DE97NGSUKBR112984 DE70NGSUKBR112985 DE43NGSUKBR112986 DE16NGSUKBR112987 DE86NGSUKBR112988 DE59NGSUKBR112989 DE32NGSUKBR112990 DE05NGSUKBR112991 DE75NGSUKBR112992 DE48NGSUKBR112993 DE21NGSUKBR112994 DE91NGSUKBR112995 DE64NGSUKBR112996 DE37NGSUKBR112997 DE10NGSUKBR112998 DE80NGSUKBR112999 DE53NGSUKBR113000 DE26NGSUKBR113001 DE96NGSUKBR113002 DE69NGSUKBR113003 DE42NGSUKBR113004 DE15NGSUKBR113005 DE85NGSUKBR113006 DE58NGSUKBR113007 DE31NGSUKBR113008
100287102 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
653635 46 82 133 121 84 78 31 21 32 48 26 47 59 18 113 66 71 99 101 59 55 75 77 42 27 32 41 85 90 112 75 0 32 25 42 19 42 1 20 33 7 47 64 28 32 42 66 21 25 75 60 41 18 99 33 11 7 55 29 16 53 75 95 2 31 121 1 36 43 20 30 82 56 109 92 27 62 17 35 43 15 42 26 17 112 81 126 137 31 64 64 58 47 16 84 93 31 65 55 38 20 12 38 48 51 60 58 80
102466751 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
100302278 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
645520 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
79501 0 0 5 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 
display_tab(SampleInfo)
Plate Position Row Col CellLine gRNA KO DIFF RAPA label fastQID replicate label_rep reads_per_sample
DE10NGSUKBR112901 1 A01 A 1 D62 sgNTC WT DIFF RAPA D62_sgNTC_DIFF_RAPA DE10NGSUKBR112901 1 D62_sgNTC_DIFF_RAPA_1 8167558
DE80NGSUKBR112902 1 A02 A 2 D62 sgNTC WT DIFF RAPA D62_sgNTC_DIFF_RAPA DE80NGSUKBR112902 2 D62_sgNTC_DIFF_RAPA_2 7947513
DE53NGSUKBR112903 1 A03 A 3 D62 sgNTC WT DIFF RAPA D62_sgNTC_DIFF_RAPA DE53NGSUKBR112903 3 D62_sgNTC_DIFF_RAPA_3 8927353
DE26NGSUKBR112904 1 A04 A 4 D62 sgNTC WT DIFF noRAPA D62_sgNTC_DIFF_noRAPA DE26NGSUKBR112904 1 D62_sgNTC_DIFF_noRAPA_1 6192682
DE96NGSUKBR112905 1 A05 A 5 D62 sgNTC WT DIFF noRAPA D62_sgNTC_DIFF_noRAPA DE96NGSUKBR112905 2 D62_sgNTC_DIFF_noRAPA_2 6316070
DE69NGSUKBR112906 1 A06 A 6 D62 sgNTC WT DIFF noRAPA D62_sgNTC_DIFF_noRAPA DE69NGSUKBR112906 3 D62_sgNTC_DIFF_noRAPA_3 7211176
DE42NGSUKBR112907 1 A07 A 7 D62 sg2.1 KO DIFF RAPA D62_sg2.1_DIFF_RAPA DE42NGSUKBR112907 1 D62_sg2.1_DIFF_RAPA_1 6472088
DE15NGSUKBR112908 1 A08 A 8 D62 sg2.1 KO DIFF RAPA D62_sg2.1_DIFF_RAPA DE15NGSUKBR112908 2 D62_sg2.1_DIFF_RAPA_2 6381728
DE85NGSUKBR112909 1 A09 A 9 D62 sg2.1 KO DIFF RAPA D62_sg2.1_DIFF_RAPA DE85NGSUKBR112909 3 D62_sg2.1_DIFF_RAPA_3 7515594
DE58NGSUKBR112910 1 A10 A 10 D62 sg2.1 KO DIFF noRAPA D62_sg2.1_DIFF_noRAPA DE58NGSUKBR112910 1 D62_sg2.1_DIFF_noRAPA_1 8072060
DE31NGSUKBR112911 1 A11 A 11 D62 sg2.1 KO DIFF noRAPA D62_sg2.1_DIFF_noRAPA DE31NGSUKBR112911 2 D62_sg2.1_DIFF_noRAPA_2 9132042
DE04NGSUKBR112912 1 A12 A 12 D62 sg2.1 KO DIFF noRAPA D62_sg2.1_DIFF_noRAPA DE04NGSUKBR112912 3 D62_sg2.1_DIFF_noRAPA_3 9158749
DE74NGSUKBR112913 1 B01 B 1 D62 sg2.2 KO DIFF RAPA D62_sg2.2_DIFF_RAPA DE74NGSUKBR112913 1 D62_sg2.2_DIFF_RAPA_1 8022580
DE47NGSUKBR112914 1 B02 B 2 D62 sg2.2 KO DIFF RAPA D62_sg2.2_DIFF_RAPA DE47NGSUKBR112914 2 D62_sg2.2_DIFF_RAPA_2 5290014
DE20NGSUKBR112915 1 B03 B 3 D62 sg2.2 KO DIFF RAPA D62_sg2.2_DIFF_RAPA DE20NGSUKBR112915 3 D62_sg2.2_DIFF_RAPA_3 7350899
DE90NGSUKBR112916 1 B04 B 4 D62 sg2.2 KO DIFF noRAPA D62_sg2.2_DIFF_noRAPA DE90NGSUKBR112916 1 D62_sg2.2_DIFF_noRAPA_1 7142950
DE63NGSUKBR112917 1 B05 B 5 D62 sg2.2 KO DIFF noRAPA D62_sg2.2_DIFF_noRAPA DE63NGSUKBR112917 2 D62_sg2.2_DIFF_noRAPA_2 6379496
DE36NGSUKBR112918 1 B06 B 6 D62 sg2.2 KO DIFF noRAPA D62_sg2.2_DIFF_noRAPA DE36NGSUKBR112918 3 D62_sg2.2_DIFF_noRAPA_3 6345821
DE09NGSUKBR112919 1 B07 B 7 D62 sgNTC WT noDIFF RAPA D62_sgNTC_noDIFF_RAPA DE09NGSUKBR112919 1 D62_sgNTC_noDIFF_RAPA_1 6954777
DE79NGSUKBR112920 1 B08 B 8 D62 sgNTC WT noDIFF RAPA D62_sgNTC_noDIFF_RAPA DE79NGSUKBR112920 2 D62_sgNTC_noDIFF_RAPA_2 6247879
DE52NGSUKBR112921 1 B09 B 9 D62 sgNTC WT noDIFF RAPA D62_sgNTC_noDIFF_RAPA DE52NGSUKBR112921 3 D62_sgNTC_noDIFF_RAPA_3 7651123
DE25NGSUKBR112922 1 B10 B 10 D62 sgNTC WT noDIFF noRAPA D62_sgNTC_noDIFF_noRAPA DE25NGSUKBR112922 1 D62_sgNTC_noDIFF_noRAPA_1 8143934
DE95NGSUKBR112923 1 B11 B 11 D62 sgNTC WT noDIFF noRAPA D62_sgNTC_noDIFF_noRAPA DE95NGSUKBR112923 2 D62_sgNTC_noDIFF_noRAPA_2 7710977
DE68NGSUKBR112924 1 B12 B 12 D62 sgNTC WT noDIFF noRAPA D62_sgNTC_noDIFF_noRAPA DE68NGSUKBR112924 3 D62_sgNTC_noDIFF_noRAPA_3 9158766
DE41NGSUKBR112925 1 C01 C 1 D62 sg2.1 KO noDIFF RAPA D62_sg2.1_noDIFF_RAPA DE41NGSUKBR112925 1 D62_sg2.1_noDIFF_RAPA_1 7883862
DE14NGSUKBR112926 1 C02 C 2 D62 sg2.1 KO noDIFF RAPA D62_sg2.1_noDIFF_RAPA DE14NGSUKBR112926 2 D62_sg2.1_noDIFF_RAPA_2 7134773
DE84NGSUKBR112927 1 C03 C 3 D62 sg2.1 KO noDIFF RAPA D62_sg2.1_noDIFF_RAPA DE84NGSUKBR112927 3 D62_sg2.1_noDIFF_RAPA_3 8617208
DE57NGSUKBR112928 1 C04 C 4 D62 sg2.1 KO noDIFF noRAPA D62_sg2.1_noDIFF_noRAPA DE57NGSUKBR112928 1 D62_sg2.1_noDIFF_noRAPA_1 7544453
DE30NGSUKBR112929 1 C05 C 5 D62 sg2.1 KO noDIFF noRAPA D62_sg2.1_noDIFF_noRAPA DE30NGSUKBR112929 2 D62_sg2.1_noDIFF_noRAPA_2 7622380
DE03NGSUKBR112930 1 C06 C 6 D62 sg2.1 KO noDIFF noRAPA D62_sg2.1_noDIFF_noRAPA DE03NGSUKBR112930 3 D62_sg2.1_noDIFF_noRAPA_3 7939374
DE73NGSUKBR112931 1 C07 C 7 D62 sg2.2 KO noDIFF RAPA D62_sg2.2_noDIFF_RAPA DE73NGSUKBR112931 1 D62_sg2.2_noDIFF_RAPA_1 7320431
DE46NGSUKBR112932 1 C08 C 8 D62 sg2.2 KO noDIFF RAPA D62_sg2.2_noDIFF_RAPA DE46NGSUKBR112932 2 D62_sg2.2_noDIFF_RAPA_2 6532522
DE19NGSUKBR112933 1 C09 C 9 D62 sg2.2 KO noDIFF RAPA D62_sg2.2_noDIFF_RAPA DE19NGSUKBR112933 3 D62_sg2.2_noDIFF_RAPA_3 7115292
DE89NGSUKBR112934 1 C10 C 10 D62 sg2.2 KO noDIFF noRAPA D62_sg2.2_noDIFF_noRAPA DE89NGSUKBR112934 1 D62_sg2.2_noDIFF_noRAPA_1 7618310
DE62NGSUKBR112935 1 C11 C 11 D62 sg2.2 KO noDIFF noRAPA D62_sg2.2_noDIFF_noRAPA DE62NGSUKBR112935 2 D62_sg2.2_noDIFF_noRAPA_2 7387684
DE35NGSUKBR112936 1 C12 C 12 D62 sg2.2 KO noDIFF noRAPA D62_sg2.2_noDIFF_noRAPA DE35NGSUKBR112936 3 D62_sg2.2_noDIFF_noRAPA_3 7440730
DE08NGSUKBR112937 1 D01 D 1 ReN sgNTC WT DIFF RAPA ReN_sgNTC_DIFF_RAPA DE08NGSUKBR112937 1 ReN_sgNTC_DIFF_RAPA_1 6178329
DE78NGSUKBR112938 1 D02 D 2 ReN sgNTC WT DIFF RAPA ReN_sgNTC_DIFF_RAPA DE78NGSUKBR112938 2 ReN_sgNTC_DIFF_RAPA_2 7406448
DE51NGSUKBR112939 1 D03 D 3 ReN sgNTC WT DIFF RAPA ReN_sgNTC_DIFF_RAPA DE51NGSUKBR112939 3 ReN_sgNTC_DIFF_RAPA_3 6338186
DE24NGSUKBR112940 1 D04 D 4 ReN sgNTC WT DIFF noRAPA ReN_sgNTC_DIFF_noRAPA DE24NGSUKBR112940 1 ReN_sgNTC_DIFF_noRAPA_1 6274167
DE94NGSUKBR112941 1 D05 D 5 ReN sgNTC WT DIFF noRAPA ReN_sgNTC_DIFF_noRAPA DE94NGSUKBR112941 2 ReN_sgNTC_DIFF_noRAPA_2 5928527
DE67NGSUKBR112942 1 D06 D 6 ReN sgNTC WT DIFF noRAPA ReN_sgNTC_DIFF_noRAPA DE67NGSUKBR112942 3 ReN_sgNTC_DIFF_noRAPA_3 6267235
DE40NGSUKBR112943 1 D07 D 7 ReN sg2.1 KO DIFF RAPA ReN_sg2.1_DIFF_RAPA DE40NGSUKBR112943 1 ReN_sg2.1_DIFF_RAPA_1 7119065
DE13NGSUKBR112944 1 D08 D 8 ReN sg2.1 KO DIFF RAPA ReN_sg2.1_DIFF_RAPA DE13NGSUKBR112944 2 ReN_sg2.1_DIFF_RAPA_2 7634764
DE83NGSUKBR112945 1 D09 D 9 ReN sg2.1 KO DIFF RAPA ReN_sg2.1_DIFF_RAPA DE83NGSUKBR112945 3 ReN_sg2.1_DIFF_RAPA_3 7137636
DE56NGSUKBR112946 1 D10 D 10 ReN sg2.1 KO DIFF noRAPA ReN_sg2.1_DIFF_noRAPA DE56NGSUKBR112946 1 ReN_sg2.1_DIFF_noRAPA_1 7764289
DE29NGSUKBR112947 1 D11 D 11 ReN sg2.1 KO DIFF noRAPA ReN_sg2.1_DIFF_noRAPA DE29NGSUKBR112947 2 ReN_sg2.1_DIFF_noRAPA_2 7877670
DE02NGSUKBR112948 1 D12 D 12 ReN sg2.1 KO DIFF noRAPA ReN_sg2.1_DIFF_noRAPA DE02NGSUKBR112948 3 ReN_sg2.1_DIFF_noRAPA_3 8604849
DE72NGSUKBR112949 1 E01 E 1 ReN sg2.2 KO DIFF RAPA ReN_sg2.2_DIFF_RAPA DE72NGSUKBR112949 1 ReN_sg2.2_DIFF_RAPA_1 6814818
DE45NGSUKBR112950 1 E02 E 2 ReN sg2.2 KO DIFF RAPA ReN_sg2.2_DIFF_RAPA DE45NGSUKBR112950 2 ReN_sg2.2_DIFF_RAPA_2 7117729
DE18NGSUKBR112951 1 E03 E 3 ReN sg2.2 KO DIFF RAPA ReN_sg2.2_DIFF_RAPA DE18NGSUKBR112951 3 ReN_sg2.2_DIFF_RAPA_3 5945129
DE88NGSUKBR112952 1 E04 E 4 ReN sg2.2 KO DIFF noRAPA ReN_sg2.2_DIFF_noRAPA DE88NGSUKBR112952 1 ReN_sg2.2_DIFF_noRAPA_1 7519852
DE61NGSUKBR112953 1 E05 E 5 ReN sg2.2 KO DIFF noRAPA ReN_sg2.2_DIFF_noRAPA DE61NGSUKBR112953 2 ReN_sg2.2_DIFF_noRAPA_2 5783946
DE34NGSUKBR112954 1 E06 E 6 ReN sg2.2 KO DIFF noRAPA ReN_sg2.2_DIFF_noRAPA DE34NGSUKBR112954 3 ReN_sg2.2_DIFF_noRAPA_3 6347263
DE07NGSUKBR112955 1 E07 E 7 ReN sgNTC WT noDIFF RAPA ReN_sgNTC_noDIFF_RAPA DE07NGSUKBR112955 1 ReN_sgNTC_noDIFF_RAPA_1 7676047
DE77NGSUKBR112956 1 E08 E 8 ReN sgNTC WT noDIFF RAPA ReN_sgNTC_noDIFF_RAPA DE77NGSUKBR112956 2 ReN_sgNTC_noDIFF_RAPA_2 6505596
DE50NGSUKBR112957 1 E09 E 9 ReN sgNTC WT noDIFF RAPA ReN_sgNTC_noDIFF_RAPA DE50NGSUKBR112957 3 ReN_sgNTC_noDIFF_RAPA_3 6630480
DE23NGSUKBR112958 1 E10 E 10 ReN sgNTC WT noDIFF noRAPA ReN_sgNTC_noDIFF_noRAPA DE23NGSUKBR112958 1 ReN_sgNTC_noDIFF_noRAPA_1 6984521
DE93NGSUKBR112959 1 E11 E 11 ReN sgNTC WT noDIFF noRAPA ReN_sgNTC_noDIFF_noRAPA DE93NGSUKBR112959 2 ReN_sgNTC_noDIFF_noRAPA_2 8152264
DE66NGSUKBR112960 1 E12 E 12 ReN sgNTC WT noDIFF noRAPA ReN_sgNTC_noDIFF_noRAPA DE66NGSUKBR112960 3 ReN_sgNTC_noDIFF_noRAPA_3 7595849
DE39NGSUKBR112961 1 F01 F 1 ReN sg2.1 KO noDIFF RAPA ReN_sg2.1_noDIFF_RAPA DE39NGSUKBR112961 1 ReN_sg2.1_noDIFF_RAPA_1 6704413
DE12NGSUKBR112962 1 F02 F 2 ReN sg2.1 KO noDIFF RAPA ReN_sg2.1_noDIFF_RAPA DE12NGSUKBR112962 2 ReN_sg2.1_noDIFF_RAPA_2 5751386
DE82NGSUKBR112963 1 F03 F 3 ReN sg2.1 KO noDIFF RAPA ReN_sg2.1_noDIFF_RAPA DE82NGSUKBR112963 3 ReN_sg2.1_noDIFF_RAPA_3 5811217
DE55NGSUKBR112964 1 F04 F 4 ReN sg2.1 KO noDIFF noRAPA ReN_sg2.1_noDIFF_noRAPA DE55NGSUKBR112964 1 ReN_sg2.1_noDIFF_noRAPA_1 7189688
DE28NGSUKBR112965 1 F05 F 5 ReN sg2.1 KO noDIFF noRAPA ReN_sg2.1_noDIFF_noRAPA DE28NGSUKBR112965 2 ReN_sg2.1_noDIFF_noRAPA_2 6200389
DE98NGSUKBR112966 1 F06 F 6 ReN sg2.1 KO noDIFF noRAPA ReN_sg2.1_noDIFF_noRAPA DE98NGSUKBR112966 3 ReN_sg2.1_noDIFF_noRAPA_3 7186721
DE71NGSUKBR112967 1 F07 F 7 ReN sg2.2 KO noDIFF RAPA ReN_sg2.2_noDIFF_RAPA DE71NGSUKBR112967 1 ReN_sg2.2_noDIFF_RAPA_1 6307822
DE44NGSUKBR112968 1 F08 F 8 ReN sg2.2 KO noDIFF RAPA ReN_sg2.2_noDIFF_RAPA DE44NGSUKBR112968 2 ReN_sg2.2_noDIFF_RAPA_2 6891060
DE17NGSUKBR112969 1 F09 F 9 ReN sg2.2 KO noDIFF RAPA ReN_sg2.2_noDIFF_RAPA DE17NGSUKBR112969 3 ReN_sg2.2_noDIFF_RAPA_3 6789988
DE87NGSUKBR112970 1 F10 F 10 ReN sg2.2 KO noDIFF noRAPA ReN_sg2.2_noDIFF_noRAPA DE87NGSUKBR112970 1 ReN_sg2.2_noDIFF_noRAPA_1 7050910
DE60NGSUKBR112971 1 F11 F 11 ReN sg2.2 KO noDIFF noRAPA ReN_sg2.2_noDIFF_noRAPA DE60NGSUKBR112971 2 ReN_sg2.2_noDIFF_noRAPA_2 8331662
DE33NGSUKBR112972 1 F12 F 12 ReN sg2.2 KO noDIFF noRAPA ReN_sg2.2_noDIFF_noRAPA DE33NGSUKBR112972 3 ReN_sg2.2_noDIFF_noRAPA_3 8271624
DE06NGSUKBR112973 1 G01 G 1 D244 sgNTC WT DIFF RAPA D244_sgNTC_DIFF_RAPA DE06NGSUKBR112973 1 D244_sgNTC_DIFF_RAPA_1 6649365
DE76NGSUKBR112974 1 G02 G 2 D244 sgNTC WT DIFF RAPA D244_sgNTC_DIFF_RAPA DE76NGSUKBR112974 2 D244_sgNTC_DIFF_RAPA_2 7645043
DE49NGSUKBR112975 1 G03 G 3 D244 sgNTC WT DIFF RAPA D244_sgNTC_DIFF_RAPA DE49NGSUKBR112975 3 D244_sgNTC_DIFF_RAPA_3 7704010
DE22NGSUKBR112976 1 G04 G 4 D244 sgNTC WT DIFF noRAPA D244_sgNTC_DIFF_noRAPA DE22NGSUKBR112976 1 D244_sgNTC_DIFF_noRAPA_1 2696422
DE92NGSUKBR112977 1 G05 G 5 D244 sgNTC WT DIFF noRAPA D244_sgNTC_DIFF_noRAPA DE92NGSUKBR112977 2 D244_sgNTC_DIFF_noRAPA_2 7988548
DE65NGSUKBR112978 1 G06 G 6 D244 sgNTC WT DIFF noRAPA D244_sgNTC_DIFF_noRAPA DE65NGSUKBR112978 3 D244_sgNTC_DIFF_noRAPA_3 5755497
DE38NGSUKBR112979 1 G07 G 7 D244 sg2.1 KO DIFF RAPA D244_sg2.1_DIFF_RAPA DE38NGSUKBR112979 1 D244_sg2.1_DIFF_RAPA_1 9078711
DE11NGSUKBR112980 1 G08 G 8 D244 sg2.1 KO DIFF RAPA D244_sg2.1_DIFF_RAPA DE11NGSUKBR112980 2 D244_sg2.1_DIFF_RAPA_2 8222243
DE81NGSUKBR112981 1 G09 G 9 D244 sg2.1 KO DIFF RAPA D244_sg2.1_DIFF_RAPA DE81NGSUKBR112981 3 D244_sg2.1_DIFF_RAPA_3 6513540
DE54NGSUKBR112982 1 G10 G 10 D244 sg2.1 KO DIFF noRAPA D244_sg2.1_DIFF_noRAPA DE54NGSUKBR112982 1 D244_sg2.1_DIFF_noRAPA_1 7754476
DE27NGSUKBR112983 1 G11 G 11 D244 sg2.1 KO DIFF noRAPA D244_sg2.1_DIFF_noRAPA DE27NGSUKBR112983 2 D244_sg2.1_DIFF_noRAPA_2 8398511
DE97NGSUKBR112984 1 G12 G 12 D244 sg2.1 KO DIFF noRAPA D244_sg2.1_DIFF_noRAPA DE97NGSUKBR112984 3 D244_sg2.1_DIFF_noRAPA_3 7432891
DE70NGSUKBR112985 1 H01 H 1 D244 sg2.2 KO DIFF RAPA D244_sg2.2_DIFF_RAPA DE70NGSUKBR112985 1 D244_sg2.2_DIFF_RAPA_1 8760362
DE43NGSUKBR112986 1 H02 H 2 D244 sg2.2 KO DIFF RAPA D244_sg2.2_DIFF_RAPA DE43NGSUKBR112986 2 D244_sg2.2_DIFF_RAPA_2 8888690
DE16NGSUKBR112987 1 H03 H 3 D244 sg2.2 KO DIFF RAPA D244_sg2.2_DIFF_RAPA DE16NGSUKBR112987 3 D244_sg2.2_DIFF_RAPA_3 8598242
DE86NGSUKBR112988 1 H04 H 4 D244 sg2.2 KO DIFF noRAPA D244_sg2.2_DIFF_noRAPA DE86NGSUKBR112988 1 D244_sg2.2_DIFF_noRAPA_1 9274217
DE59NGSUKBR112989 1 H05 H 5 D244 sg2.2 KO DIFF noRAPA D244_sg2.2_DIFF_noRAPA DE59NGSUKBR112989 2 D244_sg2.2_DIFF_noRAPA_2 8086371
DE32NGSUKBR112990 1 H06 H 6 D244 sg2.2 KO DIFF noRAPA D244_sg2.2_DIFF_noRAPA DE32NGSUKBR112990 3 D244_sg2.2_DIFF_noRAPA_3 6898881
DE05NGSUKBR112991 1 H07 H 7 D244 sgNTC WT noDIFF RAPA D244_sgNTC_noDIFF_RAPA DE05NGSUKBR112991 1 D244_sgNTC_noDIFF_RAPA_1 7527521
DE75NGSUKBR112992 1 H08 H 8 D244 sgNTC WT noDIFF RAPA D244_sgNTC_noDIFF_RAPA DE75NGSUKBR112992 2 D244_sgNTC_noDIFF_RAPA_2 7444314
DE48NGSUKBR112993 1 H09 H 9 D244 sgNTC WT noDIFF RAPA D244_sgNTC_noDIFF_RAPA DE48NGSUKBR112993 3 D244_sgNTC_noDIFF_RAPA_3 6600900
DE21NGSUKBR112994 1 H10 H 10 D244 sgNTC WT noDIFF noRAPA D244_sgNTC_noDIFF_noRAPA DE21NGSUKBR112994 1 D244_sgNTC_noDIFF_noRAPA_1 4543120
DE91NGSUKBR112995 1 H11 H 11 D244 sgNTC WT noDIFF noRAPA D244_sgNTC_noDIFF_noRAPA DE91NGSUKBR112995 2 D244_sgNTC_noDIFF_noRAPA_2 8465146
DE64NGSUKBR112996 1 H12 H 12 D244 sgNTC WT noDIFF noRAPA D244_sgNTC_noDIFF_noRAPA DE64NGSUKBR112996 3 D244_sgNTC_noDIFF_noRAPA_3 8262382
DE37NGSUKBR112997 2 A1 A 1 D244 sg2.1 KO noDIFF RAPA D244_sg2.1_noDIFF_RAPA DE37NGSUKBR112997 1 D244_sg2.1_noDIFF_RAPA_1 6632796
DE10NGSUKBR112998 2 A2 A 2 D244 sg2.1 KO noDIFF RAPA D244_sg2.1_noDIFF_RAPA DE10NGSUKBR112998 2 D244_sg2.1_noDIFF_RAPA_2 6565291
DE80NGSUKBR112999 2 A3 A 3 D244 sg2.1 KO noDIFF RAPA D244_sg2.1_noDIFF_RAPA DE80NGSUKBR112999 3 D244_sg2.1_noDIFF_RAPA_3 7135203
DE53NGSUKBR113000 2 A4 A 4 D244 sg2.1 KO noDIFF noRAPA D244_sg2.1_noDIFF_noRAPA DE53NGSUKBR113000 1 D244_sg2.1_noDIFF_noRAPA_1 6769717
DE26NGSUKBR113001 2 A5 A 5 D244 sg2.1 KO noDIFF noRAPA D244_sg2.1_noDIFF_noRAPA DE26NGSUKBR113001 2 D244_sg2.1_noDIFF_noRAPA_2 6858477
DE96NGSUKBR113002 2 A6 A 6 D244 sg2.1 KO noDIFF noRAPA D244_sg2.1_noDIFF_noRAPA DE96NGSUKBR113002 3 D244_sg2.1_noDIFF_noRAPA_3 7163942
DE69NGSUKBR113003 2 A7 A 7 D244 sg2.2 KO noDIFF RAPA D244_sg2.2_noDIFF_RAPA DE69NGSUKBR113003 1 D244_sg2.2_noDIFF_RAPA_1 6244331
DE42NGSUKBR113004 2 A8 A 8 D244 sg2.2 KO noDIFF RAPA D244_sg2.2_noDIFF_RAPA DE42NGSUKBR113004 2 D244_sg2.2_noDIFF_RAPA_2 5077409
DE15NGSUKBR113005 2 A9 A 9 D244 sg2.2 KO noDIFF RAPA D244_sg2.2_noDIFF_RAPA DE15NGSUKBR113005 3 D244_sg2.2_noDIFF_RAPA_3 6114278
DE85NGSUKBR113006 2 A10 A 10 D244 sg2.2 KO noDIFF noRAPA D244_sg2.2_noDIFF_noRAPA DE85NGSUKBR113006 1 D244_sg2.2_noDIFF_noRAPA_1 6591276
DE58NGSUKBR113007 2 A11 A 11 D244 sg2.2 KO noDIFF noRAPA D244_sg2.2_noDIFF_noRAPA DE58NGSUKBR113007 2 D244_sg2.2_noDIFF_noRAPA_2 7619920
DE31NGSUKBR113008 2 A12 A 12 D244 sg2.2 KO noDIFF noRAPA D244_sg2.2_noDIFF_noRAPA DE31NGSUKBR113008 3 D244_sg2.2_noDIFF_noRAPA_3 6147586

Total number of samples overlapping between Counts and SampleInfo: 108

boxplot_counts = function(plotsubset, maintitle, colorcode) {
  par(mar=c(3,3,5,7))
  a =boxplot(log2(plotsubset+1), main = maintitle, 
             col = Dark8[as.factor(SampleInfo[,colorcode])], names=NA,
             ylab = "log2 transformed", xlab="samples")
  legend("bottomleft", legend = levels(SampleInfo[,colorcode]),
         bty = "n", bg="white",
         pch = 16, col = Dark8[1:length(unique(SampleInfo[,colorcode]))])
}


barplot_counts = function(DF, maintitle, colorcode) {
  barplot(log2(DF[,"reads_per_sample"]), main = maintitle, 
          col = Dark8[as.factor(DF[,colorcode])], names="",
          ylab = "log2 transformed",xlab="samples")
  legend("bottomleft", legend = levels(DF[,colorcode]), pch = 16, 
         bty = "n", bg="white",
         col = Dark8[1:length(unique(DF[,colorcode]))])
}


boxplot_counts(Countdata, "raw counts", "CellLine")

boxplot_counts(Countdata, "raw counts", "gRNA")

barplot_counts(SampleInfo, "total reads", "CellLine")

barplot_counts(SampleInfo, "total reads", "gRNA")

plot(density(log2(rowMeans(Countdata))), main="distribution of gene expression", 
     xlab="mean log2(counts +1)")

# remove genes wich were not detected in at least 50% of the samples 
keeperidx = rowSums(Countdata>1)>nrow(SampleInfo)/2

Countdata_cl = Countdata[keeperidx, ]

rowdescription = rowdescription[as.character(row.names(Countdata_cl)),]

fullmodel = as.formula("~CellLine+gRNA+DIFF+RAPA")


ddsMat <- DESeqDataSetFromMatrix(countData = Countdata_cl,
                                 colData = SampleInfo,
                                 rowData = rowdescription,
                                 design = fullmodel)


ddsMat = estimateSizeFactors(ddsMat)
ddsMat = estimateDispersions(ddsMat)
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
reads  = as.data.frame(counts(ddsMat, normalized=T))

SDs = apply(reads, 1, sd)
keepvar = SDs>0

ddsMat <- ddsMat[keepvar,]

Nfilt = length(ddsMat)
reads  = as.data.frame(counts(ddsMat, normalized=T))

SampleInfo$reads_per_sample_cl= colSums(reads)

before cleaning

  • Average reads per samples: 7 187 519
  • Standard deviation reads per samples: 1 050 507
  • Total genes mapped: 28 395

after cleaning

  • Average reads per samples: 6 902 029
  • Standard deviation reads per samples: 491 463.8
  • Genes removed due to low reads: 14 847
  • Total genes included after filtering: 13 548

Clustering

hierarchical clustering based on the top 2000 genes by variance

log2_cpm = log2(reads+1)

varsset=apply(log2_cpm, 1, var)

cpm.sel.trans = t(log2_cpm[order(varsset,decreasing = T)[1:2000],])

rownames(cpm.sel.trans)=SampleInfo$label_rep

distance = dist(cpm.sel.trans)
hc = stats::hclust(distance, method="ward.D2")
cutN=24
clusters = cutree(hc, k=cutN)
Colors=sample(jetcolors(cutN))[clusters]

myLetters <- LETTERS[1:26]

numRow=match(SampleInfo$Row, myLetters)
numRow=numRow+(SampleInfo$Plate-1)*8
addRow=LETTERS[numRow]

Plotdata=data.frame(Rows=addRow, numRow = numRow, Cols = SampleInfo$Col, 
                    Group=clusters, Colors=Colors)

par(mar=c(15,3,5,3))
plot(as.dendrogram(hc), main=paste("Similairtiy by gene expression, guessed",cutN,"clusters"), cex=0.7)
colored_dots(colors = Colors, dend = as.dendrogram(hc), rowLabels = "cluster")

Similarity based on hcluster plot

par(mar=c(2,5,8,3))
plot(0,0, type="n", ylab="", xlab="", 
     ylim=rev(range(Plotdata$numRow))+c(1,-1), 
     xlim=range(Plotdata$Cols)+c(-1,1), xaxt="n",yaxt="n" ,
     main="plate similarity plot")
points(y=Plotdata$numRow, x=Plotdata$Cols, pch=16, cex=4, col=Plotdata$Colors)
text(y=Plotdata$numRow, x=Plotdata$Cols, labels = Plotdata$Group)
text(y=Plotdata$numRow, x=Plotdata$Cols, labels = Plotdata$Group)
axis(2, at=1:9, labels = c(paste0("P1_", LETTERS[1:8]), "P2_A"), las=1)
axis(3, at=1:12, labels = c(paste0("Col_", 1:12)), las=3)
abline(h=8.5)

sampleDistMatrix <- as.matrix(distance)

#colors for plotting heatmap
colors <- colorRampPalette(brewer.pal(9, "Spectral"))(255)

cellcol = Dark8[1:nlevels(SampleInfo$CellLine)]
names(cellcol) = levels(SampleInfo$CellLine)

gRNAcol = Dark8[c(1:nlevels(SampleInfo$gRNA))+nlevels(SampleInfo$CellLine)]
names(gRNAcol) = levels(SampleInfo$gRNA)

diffcol = brewer.pal(3,"Set1")[1:nlevels(SampleInfo$DIFF)]
names(diffcol) = levels(SampleInfo$DIFF)

rapacol = brewer.pal(3,"Set2")[1:nlevels(SampleInfo$RAPA)]
names(rapacol) = levels(SampleInfo$RAPA)

ann_colors = list(
  DIFF = diffcol, 
  RAPA = rapacol,
  gRNA = gRNAcol,
  CellLine=cellcol)


labels = SampleInfo[,c("CellLine","gRNA","DIFF", "RAPA")] %>%  
  mutate_all(as.character) %>% as.data.frame()

rownames(labels)=SampleInfo$label_rep

pheatmap(sampleDistMatrix,
         clustering_distance_rows = distance,
         clustering_distance_cols = distance,
         clustering_method = "ward.D2",
         scale ="row",
         border_color = NA, 
         annotation_row = labels,
         annotation_col = labels,
         annotation_colors = ann_colors,
         col = colors, 
         main = "Distances  normalized log2 counts")

save(ddsMat, file=paste0(output,"/dds_matrix.RData"))

PCA and MDS

# PCA
gpca <- glmpca(t(cpm.sel.trans), L = 2)
gpca.dat <- gpca$factors
gpca.dat$CellLine <- SampleInfo$CellLine
gpca.dat$gRNA <- SampleInfo$gRNA
gpca.dat$KO<- SampleInfo$KO
gpca.dat$DIFF <- SampleInfo$DIFF
gpca.dat$RAPA<- SampleInfo$RAPA

rownames(gpca.dat) = SampleInfo$labels
mds = as.data.frame(SampleInfo) %>% cbind(cmdscale(distance))

save(mds, gpca.dat, file=paste0(home, "/analysis/MDSplots/mdsplots.RData"))

# 
# ggplot(gpca.dat, aes(x = dim1, y = dim2, color = CellLine, shape = DIFF)) +
#             geom_point(size = 2)  + ggtitle("PCA with log2 counts")
# 
# ggplot(mds, aes(x = `1`, y = `2`, color = CellLine, shape = DIFF)) +
#             geom_point(size = 2)  + ggtitle("MDS with log2 counts")

rsconnect::setAccountInfo(name='molgenlab',
              token='86875F8B6550C3A26488035E69B1F18D',
              secret=shinySECRET)

rsconnect::deployApp(paste0(home, "/analysis/MDSplots"))
Preparing to deploy application...DONE
Uploading bundle for application: 3794945...DONE
Deploying bundle: 5601810 for application: 3794945 ...
Waiting for task: 1104549867
  building: Building image: 6510542
  building: Fetching packages
  building: Installing packages
  building: Installing files
  building: Pushing image: 6510542
  deploying: Starting instances
  rollforward: Activating new instances
  unstaging: Stopping old instances
Application successfully deployed to https://molgenlab.shinyapps.io/mdsplots/